A not-for-financial gain Group, IEEE is the world's biggest technological Qualified organization committed to advancing technological know-how for the advantage of humanity.
The next posts are merged in Scholar. Their blended citations are counted just for the main posting.
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下面的文章将在学术搜索中合并。它们的合并引用次数只会计入第一篇文章。
The global economic climate is dealing with by far the most tough period of local weather adjust past what's apparent within the pre-industrial age. Despite the fact that Africa's share of world greenhouse fuel (GHG) is negligible, the…
The subsequent posts are merged in Scholar. Their merged citations are counted only for the initial short article.
Toward assault-resistant route mutation for VANETs: an online and adaptive multiagent reinforcement Mastering solution
A not-for-financial gain organization, IEEE is the world's premier technological Skilled Corporation focused on advancing technology for the advantage of humanity.
A QoE-driven multicast approach with phase routing—A novel multimedia targeted traffic engineering paradigm
The present period is facing a dilemma associated with partaking the essentialities of Vitality means to attain economic prosperity as a consequence of the ensuing environmental issues. For that reason, sectors…
An optimized quantum minimal exploring algorithm with certain-accomplishment chance and its experiment simulation with Cirq
Tracking environmental sustainability pathways in Africa: Do all-natural source dependence, renewable Electricity, and technological improvements amplify or reduce the pollution noises?
Scanning radar forward-hunting superresolution imaging dependant on the Weibull distribution for just a sea-surface target
This "Cited by" rely incorporates citations to the following content articles in Scholar. The ones marked * could possibly be distinctive from the posting within the profile.
此“引用引文的出版物数量”计数包含了对学术搜索中以下文章的引用次数。带有*标记的引文可能与个人学术档案中的文章有所区别。
The best way to disturb network reconnaissance: a shifting target Jiahao Shen Islam protection solution according to deep reinforcement Understanding